{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T03:25:01Z","timestamp":1767410701938,"version":"3.48.0"},"reference-count":54,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Megacities\u2019 inherent complexity and dense populations heighten vulnerability to health crises, necessitating pandemic resilience research. This study pioneers a tailored resilience assessment framework for pandemic\u2010facing megacities, building upon a refined Tyler and Moench urban resilience model. Applying grey correlation\u2010technique for order preference by similarity to ideal solution (TOPSIS) methodology and barrier diagnosis modeling, we evaluated eight Chinese megacities. Key findings reveal: First, pandemic resilience scores exhibited fluctuating growth across all cities from 2014 to 2021. Shanghai demonstrated the most rapid improvement (23.13% increase), contrasting with Shenzhen's marginal gain (1.82%). Second, Shanghai achieved optimal coordination across systems, agents, and institutional dimensions in 2017, 2020, and 2021, whereas Shenzhen displayed the least dimension integration during 2016\u20132021. This dimensional equilibrium critically determines overall urban resilience. Finally, barrier analysis identified population scale, urban size, resource allocation efficiency, and mobility patterns as dominant resilience constraints. The findings and policy recommendations of this study can inform megacity development and pandemic response planning.<\/jats:p>","DOI":"10.1111\/risa.70102","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T02:15:06Z","timestamp":1757038506000},"page":"3626-3642","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Megacity Resilience to Pandemics: The Case of China"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1350-0760","authenticated-orcid":false,"given":"Yaru","family":"Cheng","sequence":"first","affiliation":[{"name":"School of National Safety and Emergency Management Beijing Normal University  Zhuhai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyun","family":"Si","sequence":"additional","affiliation":[{"name":"School of Public Administration and Policy Shandong University of Finance and Economics  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhikang","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Energy, Geoscience, Infrastructure, and Society Heriot\u2010Watt University  Edinburgh 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